Qwen3.6-35B-A3B-ROCmFP4_FAST-GGUF

Qwen3.6-35B-A3B — Q4_0-ROCmFP4_FAST (GGUF)

A ROCmFP4_FAST (4.25 bpw) quantization of Qwen3.6-35B-A3B, tuned for the AMD Strix Halo iGPU (Radeon 8060S, gfx1151, RDNA3.5). Quantized directly from the BF16 source with the MTP (nextn) head preserved.

⚠️ Requires the ROCmFPX fork — will NOT load in stock llama.cpp

ROCmFP4 is a custom weight format built on a Q4_0 base. This file only runs on the ROCmFPX fork of llama.cpp: https://github.com/charlie12345/ROCmFPX. Loading it in upstream llama.cpp, Ollama, LM Studio, etc. will fail. Build the fork (scripts/build-strix-rocmfp4-mtp.sh) and use its llama-server.

Why ROCmFP4_FAST

Measured on a Framework Desktop (Ryzen AI Max+ 395, Radeon 8060S gfx1151, ROCm 7.2, Vulkan backend), against Q4_K_M of the same base weights, plain decode:

Single-user, -fa on, f16 KV, Vulkan. Your mileage varies by driver/build.

Quality holds vs higher precision: in blind-graded coding benchmarks this FP4_FAST quant scored within noise of the BF16 source (same band), so the format costs little to no accuracy — the big quality lever is the reasoning mode below, not the quant.

Files

Run

# built ROCmFPX fork
llama-server \
  --model Qwen3.6-35B-A3B-Q4_0-ROCmFP4_FAST.gguf \
  -dev Vulkan0 -ngl 999 -fa on -c 131072 \
  -ctk f16 -ctv f16 --jinja --host 0.0.0.0 --port 8080

The GGUF carries an MTP (nextn) head, so speculative decoding works with --spec-type draft-mtp. On this hardware, conservative draft settings work best (e.g. --spec-draft-n-max 4 --spec-draft-p-min 0.55); high n_max tends to regress decode.

Sampling & reasoning (thinking on/off)

Recommended sampling (validated on this stack): --temp 0.7 --top-p 0.8 --top-k 20 --min-p 0 --presence-penalty 0.

Reasoning is task-dependent — it is the single most important serving choice for this model. Blind-graded coding benchmarks on this quant (0–100, same-grader; unknown-cause debugging suite + fully-specified implementation suite):

• Default: thinking OFF — --reasoning off --reasoning-format deepseek --reasoning-budget 0. Best for agentic / well-specified coding: ~8× lower latency, cleaner output, and it scores higher than thinking-on on well-specified work. The right mode for delegated "implement X with these constraints" tasks.
• Debugging / hard reasoning: thinking ON — --reasoning-format deepseek --reasoning-budget 4096. For "find why this misbehaves", novel algorithms, review/critique, or planning, reasoning is worth roughly +28 points and the extra latency. Without it the model tends to loop and leak deliberation into the answer.

Keep --reasoning-format deepseek in both modes — it ensures <think> blocks are parsed and closed correctly (Qwen3.6 can otherwise leak an unclosed <think> into the output).

Provenance & credits

• Base weights: unsloth/Qwen3.6-35B-A3B-MTP-GGUF (BF16 source).
• Quant format & tooling: ROCmFPX (llama-quantize ... Q4_0_ROCMFP4_FAST).
• License inherited from the base model.

Quantized 2026-07 for a Strix Halo inference stack.